Phase-out product demand forecasting

ABSTRACT

A system and method for generating or adjusting a demand forecast for a phased-out product is described. A demand forecast may be generated based on the historical data for the phased-out product and trends found in similar products that have been phased-out.

This application is a continuation of pending U.S. patent applicationSer. No. 11/478,752, filed Jun. 28, 2006, entitled, PHASE-OUT PRODUCTDEMAND FORECASTING.

FIELD OF INVENTION

The field of invention relates to supply chain management, andspecifically, to forecasting demand for replacement parts of a productthat has been discontinued or phased-out.

BACKGROUND

A supply chain is a network of retailers, distributors, transporters,warehouses, and suppliers that take part in the production, delivery,and sale of a product or service. Supply chain management is the processof coordinating the movement of the products or services, informationrelated to the products or services, and money among the constituentparts of a supply chain. Supply chain management also integrates andmanages key processes along the supply chain. Supply chain managementstrategies often involve the use of software to project and fulfilldemand and improve production levels.

Logistics is a sub-set of the activities involved in supply chainmanagement. Logistics includes the planning, implementation, and controlof the movement and storage of goods, services or related information.Logistics aims to create an effective and efficient flow and storage ofgoods, services, and related information from a source to the targetlocation where the product or source is to be shipped to meet thedemands of a customer.

The movement of goods and services through a supply chain often involvesthe shipment of the goods and services between the source location atwhich the product is produced or stored and the target location wherethe product is to be shipped to the wholesaler, vendor, or retailer. Theshipment of products involves a transport such as a truck, ship, orairplane and involves the planning and the arrangement of the productsto be shipped in the transport. The source location from which a set ofproducts is shipped on a transport is selected based on the availabilityof the products at the source location.

After a product has been discontinued or is otherwise not activelyproduced, it is generally necessary to maintain replacement parts for aperiod of time after production has stopped. These replacement parts arestored at different locations and in different quantities.Unfortunately, traditional forecasting models fail to accuratelyforecast the need for these replacement parts.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1 shows a flow for generating a phase-out profile according to anembodiment.

FIG. 2 illustrates an exemplary phase-out profile for a group and itshistorical data.

FIG. 3 illustrates an exemplary phase-out group profile and itshistorical data.

FIG. 4 shows a flow for generating a forecast for a phased-out productaccording to an embodiment.

FIG. 5 illustrates a long-term forecast example.

FIG. 6 illustrates an embodiment of a flow for adjusting an existingforecast.

FIG. 7 is long-term forecast adjustment example.

FIG. 8 is an embodiment of a system for long-term demand forecasting.

FIG. 9 shows an embodiment of a computing system (e.g., a computer).

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousdetails are set forth to provide a thorough understanding of the presentinvention. However, it will be apparent to one skilled in the art thatthese specific details are not required to practice the presentinvention.

A forecast for the demand of a discontinued or phased-out product may begenerated by looking at the trends of similar products (if available)and applying those trends to available demand data for that phased-outproduct. Described below are processes for generating profiles based onthese trends and applying that knowledge to generate or adjust aforecast for the future demand for that product.

Phase-Out Profile Generation

A phase-out profile models demand behavior for a product or group ofproducts that have been phased-out and are no longer used in production.A phase-out profile includes one or more of the following: historicaldemand data for the product(s); values for a demand curve for theproduct or group of products; a demand curve for the product; and/or astandard deviation for the demand curve. The values for the demand curvefor a product represent the percentage of demand for a particular periodwith respect to the demand for the product in its final year of use inproduction.

FIG. 2 illustrates an exemplary phase-out profile for a group orsub-group and its historical data. Products A, B, and C 201 werephased-out in 1994 and have similar characteristics. Products withsimilar characteristics may be grouped together. 1994 is considered theend year or end date for these products. Ten (10) total years of demanddata are available for these products—the end year and nine sub-Sequentyears of demand data. Demand data includes the number of productsshipped, sold, or similarly required for a period (in this case year).This data has been recorded for each of these products as shown in thetable 203. In this example, the demand for product A decreased by 10units per year. Of course, demand for a phased-out product may alsoincrease over time.

The total demand 205 for the group may also have a row in the table.From the total demand, the percentage of the demand of a particular yearcompared to the end year is determinable by dividing the number ofproducts demanded in a year following the end year by the number ofproducts demanded in the end year and multiplying that value by 100. Forexample, in EY+2 (end year plus 2 year or 1996) the total demand was for1430 products from this group. Dividing this demand (1430) by the demandin the end year (1900) and then multiplying by 100 shows that only 75.3%of the demand from 1994 was needed in 1996. Of course, equivalentmathematical formulae may be used to calculate this percentage. Fromthese percentages, a demand curve 209 for the group may be plotted.Additionally, a standard deviation may be calculated and stored as partof the group profile or stored separately.

FIG. 1 shows a flow for generating a phase-out profile according to anembodiment. This flow may be performed by a planning service manager orother similar software application that provides a user with a set oftools for managing the logistics of supply chain management.

The phased-out product or products that are to be profiled are selectedat 101. The selected product or products has associated data stored in astorage device such as a hard disk, database, or similar storage system.The data associated with a product may include demand history data, aphase-out group relationship/assignment (for example, which phase-outgroup the product belongs to), production end date (for example, theyear in which the product was no longer actively used in production),promotional information, and similar historical data. All or a portionof this data is retrieved from storage at 103.

A filter may be used to remove phased-out products that do not meet atleast some designated criteria (rules) at 105. Filter may help optimizethe flow by eliminating phased-out products that are not good candidatesfor long-term forecasting. Exemplary rules include, but are not limitedto, removing products that: 1) do not have a phase-out group assigned;2) do not have a production end date; 3) have a production end date thatis in the future; 4) are either a successor or predecessor product (theproduct is interchangeable with another product); 5) are involved inpromotions after the production end date; and/or 6) do not have enoughdemand history available (for example, do not have at least one year ofdemand history).

The remaining products (if a filter was applied at 105) or productsselected at 101 are sorted into a phase-out group or groups at 107.Products at particular locations with the same phase-out grouprelationship are normally grouped together. As described earlier, aphase-out group relationship is part of stored data that may beassociated with a product. A phase-out group may contain any number ofproducts and any amount of historical data. Typically, products of aphase-out group share similar characteristics (for example, are parts ofa bigger product) that likely makes their demand volumes to be similarover time. Groups may be broken down into sub-groups, with eachsub-group having of products that share the same end date. Groupedproducts and their associated data are structured as a group phase-outprofile.

If previously generated group phase-out profiles exist, they may bedeleted, archived, etc. at 107. Group phase-out profiles may be createdor processed serially, or in parallel if multiple groups are representedby the products selected at 101.

Phase-out group and/or sub-group profiles have parameters associatedwith them. These parameters are retrieved from storage at 109. Theparameters include, but are not limited to: 1) a phase-out profilelength (for example, the number of periods, such as years, for which thephase-out profile is to be applied in generating a long-term forecast);2) a minimum number of products needed for the creation of a phase-outprofile of a phase-out group or sub-group; 3) a phase-out profileminimum threshold, which is the minimum value that a profile may havefor a particular period (for example, a profile may have a minimumthreshold value of 5% of products which means that the demand cannot bemarked as lower than 5% of the end year demand); 4) a value for theminimum number of historical periods required for adjustment of along-term forecast; 5) a phase-out deviation threshold which may be usedto determine if a long-term forecast should be adjusted because theforecast was deemed too inaccurate (a standard deviation may becalculated between the actual historical data and the existing long-termforecast); and/or 6) a default phase-out profile value, which is apercentage value used to calculate future demand for periods beyondavailable historical data (for example, in a given year beyond whichhistorical data is available, a default phase-out value of 10% meansthat for each year beyond the historical data the number of neededproducts decreases by 10%). If a phase-out group does not exist, andtherefore the phased-out product is not similar to any other product,then a default phase-out profile may be used.

For a particular period, the group is checked at 111 to determine ifthere are a sufficient number of products in the group for that periodto provide an accurate and/or representative profile. Any number may bespecified as the minimum product number. A sub-group may be created foreach period (for example, each year) of the group. Products belonging toa sub-group typically share more in common than they do to otherproducts in the group.

If there are a sufficient number of products in the group for thatperiod, the historical data for that period is retrieved at 113. Forexample, if the group has five periods of data beginning with theproduction end date and ending four periods later, then the information(such as the number products needed for that period) from one of thoseperiods is retrieved and a sub-group profile is formed at 113.

In an embodiment, if the end date is not in the current year, yearlyhistory is read from the year of the end date to the last year (currentyear is the year of today). If the end date is in the current year,history and forecast data is read for the current year based on forecastperiod (not yearly). The forecast period of today will then bedetermined. History from the first period of the current year to theprevious period of today's period will be read and the forecast fromtoday's period to the last period of the year will also be read. Thesehistory and forecast data in forecast periods will be added together toform the projected history of the current year. Values for the sub-groupprofile and standard deviation are generated/calculated for the periodat 115. As described above, these values may include: historical demanddata for the product(s); values for a demand curve for the product orgroup of products; a demand curve for the product; and/or a standarddeviation for the demand curve

If the minimum number is not meet, then a sub-group is not created at119.

A determination is made if there is another period (and thereforesub-group) to profile at 117. If there is, then another sub-groupprofile and standard deviation is calculated for a different period ofthe group. Of course, a sub-group may contain information about morethan one phased-out product.

A phase-out profile and standard deviation for the entire group isgenerated at 121. In one embodiment, all of the sub-group phase-outprofiles and standard deviations are averaged to generate a single groupprofile. In another embodiment, weighted averaging of the sub-groups isused to help prevent an abnormal period or sub-group from skewing thephase-out profile.

The group phase-out profile and standard deviation for the locationproduct is generated/saved at 123. Phase-out profiles may be saved asseparate files and/or joined into a single group file. A phase-outprofile is typically a data structure embodied in a file such as aneXtended Markup Language (XML), Microsoft Excel file, or any other textbased file.

A determination if there are more groups to generate profiles for ismade at 125. If another group is to be profiled, then the respectivegroup parameters, if available, are read from storage at 109, etc.

The process of generating group phase-out profiles is repeatableperiodically, on receipt of updated demand data, upon request by a user,etc. Updated demand data may be received and made available immediatelyor over time. For example, additional demand data for a sub-groupreceived from a retailer may trigger a regeneration of a group phase-outprofile performed at that time or the generation of the group phase-outprofile may be performed at a later time when more information aboutother sub-groups or groups have been received from the retailer. In anembodiment, if no “new” products have been added or updated, then thegroup phase-out profile is not updated.

FIG. 3 illustrates an exemplary phase-out group profile and itshistorical data. Five sub-groups (five different end years), and theirassociated percentages for the demand of a particular year as comparedto their respective end year, are shown in the table 301. For example,for products associated with the end year of 1995, the demand for thefirst year after the end year (in other words 1996) was 96% of thedemand for the end year, the second year after the end year 85.3%, etc.From these sub-group values, total demand curve percentages 303 may becalculated, a phase-out curve plotted 305, and/or a standard deviationfor the group calculated.

FIG. 4 shows a flow for generating a forecast for a phased-out productaccording to an embodiment. A forecast is a set of expected demandvalues of a product over a selected period of time. Using the forecast,the number of products that should be shipped to or held at a locationmay be determined. Of course, other actions (such as how many productsto make, how to manage space at a particular location, etc.) may beperformed based on forecast information. The flow of FIG. 4 may beperformed by a planning service manager or other similar softwareapplication that provides a user with a set of tools for managing thelogistics of supply chain management.

The phased-out product or products that are to be forecasted areselected at 401. The production end date associated with the product(s)is retrieved at 402. A filter may be used to remove phased-out productsthat do not meet at least some designated criteria (rules) at 403.Exemplary rules include, but are not limited to, removing productsthat: 1) do not have a phase-out group assigned; 2) do not have aproduction end date; 3) have a production end date that is in thefuture; and/or 4) is not a product associated with a particularlocation. Filtering may also determine if the products or productsbelong to a group.

The selected product or products has associated data which may be storedin a storage device such as a hard disk, database, or similar storagesystem. The data associated with a product may include demand historydata, a phase-out group relationship/assignment, a production end date,promotional information, a phase-out group profile, and similarhistorical data.

The phase-out group parameters are retrieved at 405. Special interestmay be paid to the default phase-out profile value for the calculationof the forecast.

The phase-out profile associated with the product's group is retrievedat 407. This profile has the standard deviation, demand curve, forecast(if any), etc. for the group as described earlier.

If the phase-out profile has a forecast, that forecast is retrieved at409. However, this forecast may be later adjusted and/or overwritten.

The historical data associated with the product at the end date isretrieved at 411. These are the base values on which a new forecast isbased. For example, if the demand in the end year for 700 products, thenthe forecast would use that value as the starting point for successiveyears.

A determination as to which type of forecasting service is to beperformed is made at 413. If no forecast exists or the number ofhistorical periods recommended before an adjustment of a long-termforecast has not passed, then a “new” forecast will be run at 415. Theforecast is performed by applying the phase-out profile (demand curvevalues and possibly the standard deviation values) to availablehistorical data for the product beginning from the end date and lastingfor the number of periods defined by the phase-out group profile (if sodefined). If the length of the desired forecast is longer than therephase-out profile values available, then an exponential extrapolation(using one or more of the last phase-out profile values) and/or defaultvalues may be used to further forecast. The minimum phase-out thresholdmay also be taken into account and values not allowed to go below thisthreshold.

FIG. 5 illustrates a long-term forecast example. In this example, thephased-out product had a demand of 1000 products in its end year. Byapplying the demand curve of FIG. 3 to this number, the first year afterthe product's end year is predicted to have 816 products demanded, thesecond year after the end year 645 products, etc. The standard deviationfor the group may also be taken into account. Also, forecasts may bemade to any significant digit but are typically represented by wholenumbers.

An adjustment to an existing forecast may be made at 417. Adjustmentsprovide for a more accurate forecast that is based on historical demanddata that was not available at the time of the previous forecast. Forexample, if two years have passed since the previous forecast, a moreaccurate forecast may be made using the additional actual demand datafrom those two years. An adjustment to a forecast is performed bycomparing the available historical data (the actual number of productsdemanded) to the previous forecast. In an embodiment, the comparisonbegins with the year after to the end year and concludes with the yearbefore the current year. Of course, other lengths of comparisons may bemade. Additionally, in one embodiment, an adjustment cannot be madeuntil a minimum number of historical periods beyond the end date havepassed.

FIG. 6 illustrates an embodiment of a flow for adjusting an existingforecast. The difference (as a signed adjustment percentage) between thehistorical (actual) data and the forecast for all of the historicalperiods is performed at 601. The signed deviation may be calculated bythe following formula (I):

$\begin{matrix}{\frac{{{Historical}\mspace{14mu} {Data}} - {{Forecasted}\mspace{14mu} {Data}}}{{Forecasted}\mspace{14mu} {Data}}*100} & (1)\end{matrix}$

The average of the signed percentages is calculated at 603. This averageis then applied to the previous forecast at 605. This application may becalculated by the as: Forecasted Data*(100+Adjustment Percentage)/100.

FIG. 7 is long-term forecast adjustment example. This example builds offof the previous forecast shown in FIG. 5. Three years of additional data701 have been gathered and should help provide a more accurate futureforecast. Variances 703 have been calculated for these three years alongwith a percentage over/under from the previous year. The sum of thesesigned percentages is divided by the number of data points (in this casethree). In this example, the adjustment percentage is (9.33+19.4+26.1)/3or 18.3%. The old forecast is adjusted by this percentage.

The forecast is temporarily saved at 419. A determination is made ifthere are more products in the group to forecast at 421. If there are,then the products are filtered again at 403. If not, then all of theprevious forecasts for the group are combined and saved at 423.

FIG. 8 is an embodiment of a system for long-term demand forecasting.Central server 801 includes one or more processors 813, communicationsdevices 817 (such as an Ethernet card, modem, etc.), and working memory815 (such as RAM). The planning services manager 819 of the workingmemory 815 includes a forecast module 821 to generate and/or adjustphased-out product forecasts (as described earlier) and a groupphase-out profile generator 823 to generate a phase-out profile (asdescribed earlier). Of course, the forecast module 821 and groupphase-out profile generator 823 may be separate from the planningservices manager 819. The forecast module 821 and profile generator 823have access to the database 831 for product data 825 and profile data827. Files 835 associated with these applications may be stored in afile system 833 in communication with a set of processors 813. Storagefor the file system 833 and/or database 831 may be of any type,including, but not limited to, one or more physical storage devices suchas fixed disks, optical storage mediums, and magnetic storage mediums.Database 831 may be a relational database or object oriented database.

Local nodes 837, 803 may include local applications 805 or interfaces807 which include a portion of the functionality provided by the centralserver 801 such as forecast modules and/or profile generators. Localnodes 837, 803 may also be configured to communicate through a network811 to the central server 801. Through this communication the localnodes 837, 803 may call functions of the forecast module 821 and profilegenerator 823, and retrieve data from the storage system.

FIG. 9 shows an embodiment of a computing system (e.g., a computer). Theexemplary computing system of FIG. 9 includes: 1) one or more processors901; 2) a memory control hub (MCH) 902; 3) a system memory 903 (of whichdifferent types exist such as DDR RAM, EDO RAM, etc,); 4) a cache 904;5) an I/O control hub (ICH) 905; 6) a graphics processor 906; 7) adisplay/screen 907 (of which different types exist such as Cathode RayTube (CRT), Thin Film Transistor (TFT), Liquid Crystal Display (LCD),Digital Light Processing (DLP), Organic LED (OLED), etc.; and 8) one ormore I/O and storage devices 908.

The one or more processors 901 execute instructions to perform whateversoftware routines the computing system implements. The instructionsfrequently involve some sort of operation performed upon data. Both dataand instructions are stored in system memory 903 and cache 904. Cache904 is typically designed to have shorter latency times than systemmemory 903. For example, cache 1604 might be integrated onto the samesilicon chip(s) as the processor(s) and/or constructed with faster SRAMcells whilst system memory 903 might be constructed with slower DRAMcells. By tending to store more frequently used instructions and data inthe cache 904 as opposed to the system memory 903, the overallperformance efficiency of the computing system improves.

System memory 903 is deliberately made available to other componentswithin the computing system. For example, the data received from variousinterfaces to the computing system (e.g., keyboard and mouse, printerport, LAN port, modem port, etc.) or retrieved from an internal storageelement of the computing system (e.g., hard disk drive) are oftentemporarily queued into system memory 903 prior to their being operatedupon by the one or more processor(s) 901 in the implementation of asoftware program. Similarly, data that a software program determinesshould be sent from the computing system to an outside entity throughone of the computing system interfaces, or stored into an internalstorage element, is often temporarily queued in system memory 903 priorto its being transmitted or stored.

The ICH 905 is responsible for ensuring that such data is properlypassed between the system memory 903 and its appropriate correspondingcomputing system interface (and internal storage device if the computingsystem is so designed). The MCH 902 is responsible for managing thevarious contending requests for system memory 903 access amongst theprocessor(s) 901, interfaces and internal storage elements that mayproximately arise in time with respect to one another.

One or more I/O devices 908 are also implemented in a typical computingsystem. I/O devices generally are responsible for transferring data toand/or from the computing system (e.g., a networking adapter); or, forlarge scale non-volatile storage within the computing system (e.g., harddisk drive). ICH 905 has bi-directional point-to-point links betweenitself and the observed I/O devices 908. A capture program,classification program, a database, a filestore, an analysis engineand/or a graphical user interface may be stored in a storage device ordevices 908 or in memory 903.

Portions of what was described above may be implemented with logiccircuitry such as a dedicated logic circuit or with a microcontroller orother form of processing core that executes program code instructions.Thus processes taught by the discussion above may be performed withprogram code such as machine-executable instructions that cause amachine that executes these instructions to perform certain functions.In this context, a “machine” may be a machine that converts intermediateform (or “abstract”) instructions into processor specific instructions(e.g., an abstract execution environment such as a “virtual machine”(e.g., a Java Virtual Machine), an interpreter, a Common LanguageRuntime, a high-level language virtual machine, etc.)), and/or,electronic circuitry disposed on a semiconductor chip (e.g., “logiccircuitry” implemented with transistors) designed to executeinstructions such as a general-purpose processor and/or aspecial-purpose processor. Processes taught by the discussion above mayalso be performed by (in the alternative to a machine or in combinationwith a machine) electronic circuitry designed to perform the processes(or a portion thereof) without the execution of program code.

It is believed that processes taught by the discussion above may also bedescribed in source level program code in various object-orientated ornon-object-orientated computer programming languages (e.g., Java, C#,VB, Python, C, C++, J#, APL, Cobol, ABAP, Fortran, Pascal, Perl, etc.)supported by various software development frameworks (e.g., MicrosoftCorporation's .NET, Mono, Java, Oracle Corporation's Fusion, etc.). Thesource level program code may be converted into an intermediate form ofprogram code (such as Java byte code, Microsoft Intermediate Language,etc.) that is understandable to an abstract execution environment (e.g.,a Java Virtual Machine, a Common Language Runtime, a high-level languagevirtual machine, an interpreter, etc.), or a more specific form ofprogram code that is targeted for a specific processor.

An article of manufacture may be used to store program code. An articleof manufacture that stores program code may be embodied as, but is notlimited to, one or more memories (e.g., one or more flash memories,random access memories (static, dynamic or other)), optical disks,CD-ROMs, DVD ROMs, EPROMs, EEPROMs, magnetic or optical cards or othertype of machine-readable media suitable for storing electronicinstructions. Program code may also be downloaded from a remote computer(e.g., a server) to a requesting computer (e.g., a client) by way ofdata signals embodied in a propagation medium (e.g., via a communicationlink (e.g., a network connection)).

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. A machine readable medium having instructions stored therein whichwhen executed cause a machine to perform a set of operations comprising:retrieving data about a phased-out product from a storage system;retrieving a group phase-out profile associated with the phased-outproduct the group phase-out profile generated by sorting a plurality ofphased-out products into groups and aggregating a phase-out profile foreach group member; and forecasting demand for the phased-out productbased on the group phase-out profile and retrieved data.
 2. The machinereadable medium of claim 1, further comprising: determining a group forthe phased-out product.
 3. The machine readable medium of claim 2,further comprising: forecasting demand for the group.
 4. The machinereadable medium of claim 1, wherein retrieving data comprises:retrieving a standard deviation value for the phased-out product.
 5. Themachine readable medium of claim 3, further comprising: calculating ademand forecast range for a time period using the standard deviationvalue.
 6. The machine readable medium of claim 1, further comprising:generating a phase-out profile associated with the phased-out product.7. A system comprising: a storage system to store product data and agroup phase-out profile for a phased-out product the group phase-outprofile generated by sorting a plurality of phased-out products intogroups and aggregating a phase-out profile for each group member; aforecast module to communicate with the persistent storage system, theforecast module to retrieve the group phase-out profile from thepersistent storage system and to generate a demand forecast for aphased-out product based on the phase-out group profile.
 8. The systemof claim 7, further comprising: a profile generation module tocommunicate with the storage system, the profile generation module togenerate a phase-out profile based on the demand history for thephased-out product.
 9. The system of claim 8, wherein the profilegeneration module generates the phase-out profile if a minimum number ofdata requirements are met.
 10. The system of claim 8, wherein theprofile generation module generates the group phase-out profile fordevices with similar traits.
 11. The system of claim 8, wherein theprofile generation module generates a standard deviation for thephase-out profile.
 12. The system of claim 7, further comprising: acommunication device to enable access to the demand forecast from aremote location.
 13. The system of claim 7, wherein the forecast moduleis located on a central server, further comprising: a local machine toexecute an interface to access the forecast module over a network.
 14. Amethod comprising: generating a group phase-out profile for a phased-outproduct by aggregating a phase-out profile for the phased-out productwith other phase-out profiles from an assigned group; and generating ademand forecast for the product associated with the group phase-outprofile.
 15. The method of claim 14, further comprising: gathering dataabout the phased-out product after the generating of the demandforecast; and adjusting the demand forecast based on the data gatheredafter the generation of the forecast.
 16. The method of claim 14,wherein generating a demand forecast for the product associated with thephase-out profile further comprises: retrieving parameters andhistorical data associated with the product; applying the values of thephase-out profile to the historical data associated with the product.17. The method of claim 14, further comprising: filtering the pluralityof phased-out products prior to the sorting.
 18. The method of claim 17,wherein the filtering removes any one of ungrouped phased-out products,phased-out products without a production end date, phased-out productswith a production end date in the future.